Ptychographic imaging with a fiber endoscope via wavelength scanning

Ptychography has become a popular computational imaging method for microscopy in recent years. In the present work we employ a wavelength scanning ptychography technique enhanced by neural networks for imaging with a fiber endoscope. Illumination of the object at various wavelengths is achieved using a single mode fiber, while a multicore fiber collects diffracted light from a distance. Using a U-Net multilayer convolutional neural network, the diffraction pattern is recovered at the far end of the multicore fiber from the recorded intensity pattern at the proximal end. With the recovered diffraction pattern in place, the phase object can be reconstructed using the ptychography algorithm. The quality of the object reconstruction improves with the number of wavelengths used. Comparison with an end-to-end neural network highlights the effectiveness and practicality of this two-step hybrid system. This alternative and simplified ptychographic endoscopy setup delivers noticeable improvements through neural networks and wavelength scanning.